Spam is an ever-increasing problem. The number of spam mails is increasing daily - in June 2003, studies showed that over 50% of all email is spam. Added to this, spammers are becoming more sophisticated and are constantly managing to outsmart 'static' methods of fighting spam.

The techniques currently used by anti-spam software are static, meaning that it is fairly easy to evade by tweaking the message a little. To do this, spammers simply examine the latest anti-spam techniques and find ways how to dodge them.

To effectively combat spam, an adaptive new technique is needed. This method must be familiar with spammers' tactics as they change over time. It must also be able to adapt to the particular organization that it is protecting from spam.

The answer lies in Bayesian mathematics, which can be applied to the spam problem, resulting in an adaptive, ‘statistical intelligence’ technique that is much harder to circumvent by spammers. The Bayesian approach is the only and best way to tackle spam once and for all, as it overcomes the obstacles faced by more static technologies such as blacklist checking, databases of known spam and keyword checking. This is not to say that these technologies are obsolete, but they cannot be used as effectively as needed if not combined with a Bayesian filter.

How the Bayesian spam filter works

Bayesian filtering is based on the principle that most events are dependent and that the probability of an event occurring in the future can be inferred from the previous occurrences of that event. (More information about the mathematical basis of Bayesian filtering is available at http://www-ccrma.stanford.edu/~jos/bayes/Bayesian_Parameter_Estimation.html and http://www.niedermayer.ca/papers/bayesian/bayes.html.)

This same technique can be used to classify spam. If some piece of text occurs often in spam but not in legitimate mail, then the next time the same piece of text is encountered in a new email, it would be reasonable to assume that this email is probably spam.

Creating a tailor-made Bayesian word database

Before mail can be filtered using this method, the user needs to generate a history for each word or token (such as the $ sign, IP addresses and domains, and so on). A probability value is then assigned to each word or token; the probability is based on calculations that take into account how often that word occurs in spam as opposed to legitimate mail. This is done by analyzing the users' outbound mail and by analyzing known spam: All the words and tokens in both pools of mail are analyzed to generate the probability that a particular word is spam.

For example, if the word "mortgage" occurs in 400 of 3,000 spam mails and 5 out of 300 legitimate emails, its spam probability is:

(400/3000) / (5/300 + 400/3000) = 0.8889.

Its important to note that this analysis is performed on the company's mail, and is therefore tailored to that particular company. For example, a financial institution might use the word "mortgage" many times over would get a lot of false positives if using a general anti-spam rule set. The Bayesian filter, on the other hand, takes note of the company's valid outbound mail (and recognizes "mortgage" as being frequently used in legitimate messages), and therefore has a much better spam detection rate and a far lower false positive rate.

Once the word probabilities have been calculated, the filter is ready for use.

Note that the Bayesian filter is not static - the filter is then constantly updated based on new spam and valid emails; the Bayesian filter's performance will therefore improve over time and - more importantly - will adapt to a change in spam tactics and/or a change in the kind of emails written by users within the organization.

Finding spam based on the Bayesian filter

When a new mail arrives, it is broken down into words and the most relevant words - i.e., those that are most significant in identifying whether the mail is spam or not - are singled out. From these words the Bayesian filter calculates the probability of the new message being spam or not. If the probability is greater than a threshold, say 0.9, then the message is classified as spam.

This Bayesian approach to spam is highly effective - a May 2003 BBC article reported that spam detection rates of over 99.7% can be achieved with a very low number of false positives.

Why Bayesian filtering is better than keyword checking in detecting spam

1. The Bayesian method takes the whole message into account - It recognizes keywords that identify spam, but it also recognizes words that denote valid mail. For example: not every email that contains the word "free" and "cash" is spam. The advantage of the Bayesian method is that it considers the most interesting words (as defined by their deviation from the mean) and comes up with a probability that a message is spam. The Bayesian method would find the words "cash" and "free" interesting but it would also recognize the name of the business contact who sent the message and thus classify the message as legitimate, for instance; it allows words to "balance" each other out. In other words, Bayesian filtering is a much more intelligent approach because it examines all aspects of a message, as opposed to keyword checking that classifies a mail as spam on the basis of a single word.

2. A Bayesian filter is constantly self-adapting - By learning from new spam and new valid outbound mails, the Bayesian filter evolves and adapts to new spam techniques. For example, when spammers started using "f-r-e-e" instead of "free" they succeeded in evading keyword checking until "f-r-e-e" was also included in the keyword database. On the other hand, the Bayesian filter automatically notices such tactics; in fact if the word "f-r-e-e" is found, it is an even better spam indicator. Another example would be using the word "5ex" instead of "Sex".

3. The Bayesian technique is sensitive to the user - To be successful and have their messages delivered, spammers have to send emails that are not caught by the indented victims' personalized filters. Because the Bayesian method takes the company's email profile into account, it detects spam with greater ease: Spammers would need to know the company's email profile to be able to circumvent it. Because since spam mails have their own vocabulary and character, the Bayesian filter can catch them out; however, it is not easy for spammers to change their sales pitch to take an organization's email profile into account; after all, there are only so many ways to sell Viagra!

4. The Bayesian method is multi-lingual and international - A Bayesian anti-spam filter, being adaptive, can be used for any language required. Most keyword lists are only available in English only and are therefore quite useless in non English-speaking regions. The Bayesian filter can also take into account certain languages deviations or the diverse usage of certain words in different areas, even if the same language is spoken. This intelligence enables such a filter to catch more spam.

5. A Bayesian filter is hard to trick as opposed to a keyword filter - An advanced spammer who wants to trick a Bayesian filter can either use fewer 'bad' words (i.e., words that usually indicate spam such as free, Viagra, etc), or more words that generally indicate valid mail (such as a valid contact name, etc). Doing the latter is impossible because the spammer would have to know the email profile of each recipient - and a spammer can never hope to gather this kind of information from every intended recipient. Using neutral words, for example the word "public", would not work since these are disregarded in the final analysis. Breaking up spam words (using "f-r-e-e" instead of "free") will just increase the chance of the message being spam, since a legitimate user will rarely write the word "free" as "f-r-e-e".

Bayesian filters or updated keyword lists?

Some types of anti-spam software regularly download new keyword files. While this is of course better than not updating keyword lists, the fact is a rather patchy approach that is easily circumvented. Downloading updates makes it a little bit harder, but the principal system is flawed compared to a Bayesian filter.

The Bayesian filter is a new approach to spam that is likely to create a revolution in the sphere of anti-spam software - because it is both intelligent and adaptive. History has shown us that this is really the only way to deal with the complex and changing problem that is spam.

About GFI MailEssentials

GFI MailEssentials for Exchange/SMTP is a server-based anti-spam and email management solution for Microsoft Exchange Server and Notes/SMTP servers. It virtually eliminates spam from your mail server through its in-built Bayesian filter and its other key features:

• Block spam at server level - No need to update email clients
• Automatic whitelist management - Keep whitelists up-to-date without extra admin
• Blacklists scanning - Stop mail from blacklisted senders and invalid domains
• Email header analysis - Blocks spam based on message field info
• Keyword checking - Enables you to refine your anti-spam rules
• and more!

GFI MailEssentials also adds key email tools to your mail server: disclaimers, mail archiving and monitoring, reporting, server-based auto replies and POP3 downloading.

For more information and to download a free trial version, click here.

About GFI

GFI (www.gfi.com) is a leading provider of Windows-based network security, content security and messaging software. Key products include the GFI FAXmaker fax connector for Exchange and fax server for networks; GFI MailSecurity email content/exploit checking and anti-virus software; GFI MailEssentials server-based anti-spam software; GFI LANguard Security Event Log Monitor (S.E.L.M.) that performs event log based intrusion detection and network-wide event log management; and GFI LANguard Network Security Scanner (N.S.S.) that audits network security and allows administrators to remotely install hotfixes and service packs. Clients include Microsoft, Telstra, Time Warner Cable, Shell Oil Lubricants, NASA, DHL, Caterpillar, BMW, the US IRS, and the USAF. GFI has six offices in the US, UK, Germany, France, Australia and Malta, and has a worldwide network of distributors. GFI is a Microsoft Gold Certified Partner and has won the Microsoft Fusion (GEM) Packaged Application Partner of the Year award.